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81 lines
2.0 KiB
Python
81 lines
2.0 KiB
Python
3 years ago
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"""Fit probability distributions to IPCC sea level rise forecasts.
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Reads:
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'IPCC AR6.xlsx'
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Writes:
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'png/*.png'
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D. Howe
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d.howe@wrl.unsw.edu.au
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2022-05-12
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"""
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import os
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import re
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import numpy as np
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import pandas as pd
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from scipy import stats, optimize
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import matplotlib.pyplot as plt
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# Read data
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df = pd.read_excel('IPCC AR6.xlsx', index_col=[0, 1, 2, 3, 4])
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df = df.sort_index()
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# Use all 'medium' confidence scenarios for intermediate quantiles
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scenarios = ['ssp119', 'ssp126', 'ssp245', 'ssp370', 'ssp585']
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dff = df.loc[838, 'total', 'medium', scenarios].groupby('quantile').mean()
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# Use ssp119/ssp585 for 5th and 95th quantiles
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dff.loc[5] = df.loc[838, 'total', 'medium', 'ssp119', 5]
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dff.loc[95] = df.loc[838, 'total', 'medium', 'ssp585', 95]
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dff = dff.T
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dff.index.name = 'year'
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percentiles = dff.columns.values / 100
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values = dff.loc[2150].values
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x_min = values.min() - 0.2
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x_max = values.max() + 0.2
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x = np.linspace(x_min, x_max, num=1000)
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# Get statistical distributions
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distributions = [
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getattr(stats, d) for d in dir(stats)
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if isinstance(getattr(stats, d), stats.rv_continuous)
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]
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for dist in distributions:
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def cdf(x, loc, scale):
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"""Calculate cumulative density function"""
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return dist(loc=loc, scale=scale).cdf(x)
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try:
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loc, scale = optimize.curve_fit(cdf, values, percentiles)[0]
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p = {'loc': loc, 'scale': scale}
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except TypeError:
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continue
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fig, ax = plt.subplots(1, 2, figsize=(6, 2))
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ax[0].plot(x, 100 * dist.cdf(x, **p))
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ax[0].plot(values, 100 * percentiles, '.', c='#444444')
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ax[1].plot(x, 100 * dist.pdf(x, **p))
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ax[0].axhline(y=100, c='#000000', lw=0.8, zorder=-1)
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ax[0].set_ylim(0, 101)
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ax[1].set_ylim(bottom=0)
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ax[0].set_title(dist.name, x=-0.7, y=0.5, ha='left')
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for a in ax.ravel():
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a.spines['right'].set_visible(False)
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a.spines['top'].set_visible(False)
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ax[1].set_yticks([])
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plt.savefig(f'png/{dist.name}.png', bbox_inches='tight', dpi=100)
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plt.close()
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